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English(EN) Data informed Ansatz Selection for Variational Quantum Circuits

新方法仅用数据优化变分量子电路

研究人员开发了一种数据驱动的方法来选择变分量子电路(VQC),无需对大量候选电路进行广泛训练。该方法使用基于数据集傅里叶系数和量子费舍尔信息矩阵的三条规则来确定最佳 Ansatz 和深度。对于 Madelon 数据集,该方法在没有传统网格搜索的情况下,设计了一个具有 239 个参数的分类器,测试准确率为 78.5%,并且消融实验证实了预测电路组件的有效性。 AI

排序理由 该条目描述了一篇详细介绍优化量子电路新方法的研究论文。[lever_c_demoted from research: ic=1 ai=0.4]

在 Hugging Face Daily Papers 阅读 →

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新方法仅用数据优化变分量子电路

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该条目描述了一篇详细介绍优化量子电路新方法的研究论文。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    数据驱动的变分量子电路方法选择

    Choosing a variational quantum circuit (VQC) usually means searching over ansatze and depths, and training each candidate. We give three rules that fix these choices from the data and the gate set alone, before any training. First, a circuit without entangling gates has a rank-on…